Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/12975
Title: Biomedical instrumentation of photoacoustic imaging and quantitative sensing for clinical applications
Authors: Khan, Suhel
Vasudevan, Srivathsan
Issue Date: 2023
Publisher: American Institute of Physics Inc.
Citation: Kushwaha, R., Singh, M. K., Krishnan, S., & Rai, D. K. (2023). Machine learning enabled property prediction of carbon-based electrodes for supercapacitors. Journal of Materials Science. Scopus. https://doi.org/10.1007/s10853-023-08981-8
Abstract: Photoacoustic (PA) imaging has been well researched over the last couple of decades and has found many applications in biomedical engineering. This has evinced interest among many scientists in developing this as a compact instrument for biomedical diagnosis. This review discusses various instrumentation developments for PA experimental setups and their applications in the biomedical diagnostic field. It also covers the PA spectral response or PA sensing technique, which uses the spectral information of the PA signal and performs sensing to deliver a fast, cost-effective, and compact screening tool instead of imaging. Primarily, this review provides an overview of PA imaging concepts and the development of hardware instrumentation systems in both the excitation and acquisition stages of this technique. Later, the paper discusses PA sensing, the quantitative spectral parameter extraction from the PA spectrum, and the correlation study of the spectral parameters with the physical parameters of the tissue. This PA sensing technique was used to diagnose various diseases, such as thyroid nodules, breast cancer, renal disorders, and zoonotic diseases, based on the mechanical and biological characteristics of the tissues. The paper culminates with a discussion section that provides future developments that are necessary to take this technique into clinical applications as a quantitative PA imaging technique. © 2023 Author(s).
URI: https://doi.org/10.1063/5.0151882
https://dspace.iiti.ac.in/handle/123456789/12975
ISSN: 0034-6748
Type of Material: Review
Appears in Collections:Department of Electrical Engineering

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